# Research Script API Usage

See [`api-reference.md`](api-reference.md) for the full DataAPI and ChartingAPI source with all method signatures and docstrings. See [`pandas-ta-reference.md`](pandas-ta-reference.md) for the indicator catalog.

Research scripts executed via the `ExecuteResearch` MCP tool have access to the global API instance, which provides both data fetching and charting capabilities.

## Accessing the API

```python
from dexorder.api import get_api
import asyncio

# Get the global API instance
api = get_api()
```

## Using the Data API

The data API provides access to historical OHLC (Open, High, Low, Close) market data with smart caching via Iceberg.

### Fetching Historical Data

The API accepts flexible timestamp formats for convenience:

```python
from dexorder.api import get_api
import asyncio
from datetime import datetime

api = get_api()

# Method 1: Using Unix timestamps (seconds)
# 1609459200 = 2021-01-01, 1735689600 = 2025-01-01
df = asyncio.run(api.data.historical_ohlc(
    ticker="BTC/USDT.BINANCE",
    period_seconds=3600,  # 1 hour candles
    start_time=1609459200,  # 2021-01-01
    end_time=1735689600,    # 2025-01-01 (~4 years, ~35,000 bars)
    extra_columns=["volume"]
))

# Method 2: Using date strings
df = asyncio.run(api.data.historical_ohlc(
    ticker="BTC/USDT.BINANCE",
    period_seconds=3600,
    start_time="2021-01-01",
    end_time="2025-01-01",   # ~4 years of 1h bars ≈ 35,000 bars
    extra_columns=["volume"]
))

# Method 3: Using date strings with time
df = asyncio.run(api.data.historical_ohlc(
    ticker="BTC/USDT.BINANCE",
    period_seconds=3600,
    start_time="2021-01-01 00:00:00",
    end_time="2025-01-01 00:00:00",
    extra_columns=["volume"]
))

# Method 4: Using datetime objects
from datetime import datetime, timedelta
end_time = datetime.now()
start_time = end_time - timedelta(days=4*365)  # 4 years back
df = asyncio.run(api.data.historical_ohlc(
    ticker="BTC/USDT.BINANCE",
    period_seconds=3600,
    start_time=start_time,
    end_time=end_time,
    extra_columns=["volume"]
))

print(f"Loaded {len(df)} candles from {df.index[0]} to {df.index[-1]}")
print(df.head())
```

### Available Extra Columns

- `"volume"` - Total volume
- `"buy_vol"` - Buy-side volume
- `"sell_vol"` - Sell-side volume
- `"open_time"`, `"high_time"`, `"low_time"`, `"close_time"` - Timestamps for each price point
- `"open_interest"` - Open interest (for futures)
- `"ticker"` - Market identifier
- `"period_seconds"` - Period in seconds

## Using the Charting API

The charting API provides styled financial charts with OHLC candlesticks and technical indicators.

### Creating a Basic Candlestick Chart

```python
from dexorder.api import get_api
import asyncio
from datetime import datetime

api = get_api()

# Fetch data
df = asyncio.run(api.data.historical_ohlc(
    ticker="BTC/USDT.BINANCE",
    period_seconds=3600,
    start_time="2021-01-01",
    end_time="2025-01-01",   # ~4 years of 1h bars
    extra_columns=["volume"]
))

# Create candlestick chart (synchronous)
fig, ax = api.charting.plot_ohlc(
    df,
    title="BTC/USDT 1H",
    volume=True,  # Show volume bars
    style="charles"  # Chart style
)

# The figure is automatically captured and returned to the MCP client
```

### Adding Indicator Panels

Use **pandas-ta** for all indicator calculations. Do not write manual rolling/ewm implementations.

```python
from dexorder.api import get_api
import asyncio
import pandas_ta as ta

api = get_api()

# Fetch data
df = asyncio.run(api.data.historical_ohlc(
    ticker="BTC/USDT.BINANCE",
    period_seconds=3600,
    start_time="2021-01-01",
    end_time="2025-01-01"
))

# Calculate indicators using pandas-ta
df['sma_20'] = ta.sma(df['close'], length=20)
df['rsi'] = ta.rsi(df['close'], length=14)

# Create chart
fig, ax = api.charting.plot_ohlc(df, title="BTC/USDT with SMA")

# Overlay the SMA on the price chart
# NOTE: mplfinance uses integer x-positions (0..N-1); use range(len(df)), not df.index.
ax.plot(range(len(df)), df['sma_20'], label="SMA 20", color="blue", linewidth=2)
ax.legend()

# Add RSI indicator panel below
rsi_ax = api.charting.add_indicator_panel(
    fig, df,
    columns=["rsi"],
    ylabel="RSI",
    ylim=(0, 100)
)
rsi_ax.axhline(70, color='red', linestyle='--', alpha=0.5)
rsi_ax.axhline(30, color='green', linestyle='--', alpha=0.5)
```

### Multi-Output Indicators

Some pandas-ta indicators return a DataFrame. Extract the columns you need:

```python
import pandas_ta as ta

# MACD returns: MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9
macd_df = ta.macd(df['close'], fast=12, slow=26, signal=9)
df['macd'] = macd_df.iloc[:, 0]      # MACD line
df['macd_hist'] = macd_df.iloc[:, 1] # Histogram
df['macd_signal'] = macd_df.iloc[:, 2] # Signal line

# Bollinger Bands returns: BBL, BBM, BBU, BBB, BBP
bb_df = ta.bbands(df['close'], length=20, std=2.0)
df['bb_upper'] = bb_df.iloc[:, 2]  # BBU
df['bb_mid']   = bb_df.iloc[:, 1]  # BBM
df['bb_lower'] = bb_df.iloc[:, 0]  # BBL

# Stochastic returns: STOCHk, STOCHd
stoch_df = ta.stoch(df['high'], df['low'], df['close'], k=14, d=3, smooth_k=3)
df['stoch_k'] = stoch_df.iloc[:, 0]
df['stoch_d'] = stoch_df.iloc[:, 1]

# ATR (uses high, low, close)
df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
```

## Complete Example

```python
from dexorder.api import get_api
import asyncio
import pandas_ta as ta

# Get API instance
api = get_api()

# Fetch historical data — use max history for research (target 100k-200k bars)
from datetime import datetime, timedelta
end_time = datetime.now()
start_time = end_time - timedelta(days=3*365)  # 3 years of 1h bars ≈ 26,000 bars

df = asyncio.run(api.data.historical_ohlc(
    ticker="BTC/USDT.BINANCE",
    period_seconds=3600,  # 1 hour
    start_time=start_time,
    end_time=end_time,
    extra_columns=["volume"]
))
print(f"[Data] {len(df)} bars | {df.index[0]} → {df.index[-1]} | period=3600s")

# Add moving averages using pandas-ta
df['sma_20'] = ta.sma(df['close'], length=20)
df['ema_50'] = ta.ema(df['close'], length=50)

# Create chart with volume
fig, ax = api.charting.plot_ohlc(
    df,
    title="BTC/USDT Analysis",
    volume=True,
    style="charles"
)

# Overlay moving averages
# NOTE: mplfinance uses integer x-positions (0..N-1); use range(len(df)), not df.index.
ax.plot(range(len(df)), df['sma_20'], label="SMA 20", color="blue", linewidth=1.5)
ax.plot(range(len(df)), df['ema_50'], label="EMA 50", color="red", linewidth=1.5)
ax.legend()

# Print summary statistics
print(f"[Data] {len(df)} bars | {df.index[0]} → {df.index[-1]} | period=3600s")
print(f"High: {df['high'].max()}")
print(f"Low: {df['low'].min()}")
print(f"Mean Volume: {df['volume'].mean():.2f}")
```

## Notes

- **Async vs Sync**: Data API methods are async and require `asyncio.run()`. Charting API methods are synchronous.
- **Figure Capture**: All matplotlib figures created during script execution are automatically captured and returned as PNG images.
- **Print Statements**: All `print()` output is captured and returned as text content.
- **Errors**: Exceptions are caught and reported in the execution results.
- **Timestamps**: The API accepts flexible timestamp formats:
  - Unix timestamps in **seconds** (int or float) - e.g., `1640000000`
  - Date strings - e.g., `"2021-12-20"` or `"2021-12-20 12:00:00"`
  - datetime objects - e.g., `datetime(2021, 12, 20)`
  - pandas Timestamp objects
  - Internally, the system uses microseconds since epoch, but you don't need to worry about this conversion.
- **Price/Volume Values**: All prices and volumes are returned as decimal floats, automatically converted from internal storage format using market metadata. No manual conversion is needed.

## Available Chart Styles

- `"charles"` (default)
- `"binance"`
- `"blueskies"`
- `"brasil"`
- `"checkers"`
- `"classic"`
- `"mike"`
- `"nightclouds"`
- `"sas"`
- `"starsandstripes"`
- `"yahoo"`
